1. Field of the Invention
The present invention relates to face detection, and more particularly, to a face detection method and apparatus by which a face is detected accurately in real-time according to whether or not a face is detected in a previous frame image, by applying any one of a face detection mode and a face tracking mode to a current frame image, and a security monitoring system using the same.
2. Description of the Related Art
Face detection technology, which is an important component of facial recognition systems, is becoming more important as a variety of applications have been being introduced recently, including human-computer interface, video monitoring systems, and face-based image retrieval. Though a lot of research activities on face detection technologies have been performed, the reliability of algorithms is still not high enough to be applied to practical life and the detection speed is not satisfactory.
To solve the aforementioned problems, research on a method using a decision boundary, determined after training from face sample patterns, is being carried out. Representative methods in this category include multi layer perceptron (MLP) and support vector machine (SVM). The conventional MLP uses a local receptive field to apply to a face image and another conventional method projects an image pattern on a plurality of principle component analysis (PCA) subspaces and uses distances to a subspace as an MLP input. However, since the learning method through MLP only minimizes errors from given sample data, the method operates well for trained data but successful operations for new data that has not been trained cannot be guaranteed. In particular, considering various changes that occur with a face image according to factors, such as illumination, facial expressions, and poses, the reliability of the methods based on MLP is low if the number of data samples is very big.
Meanwhile, SVM can minimize errors of given data and at the same time maximize the margin of the entire facial recognition system such that the capability of the SVM generalizing a new pattern is superior to the MLP. In the conventional SVM technology, the SVM is applied to a face image without change and a face detection result with some degree of reliability is obtained. However, the SVM is not satisfactory for application to practical life or real world situations. In another conventional SVM technology, without using a face image as a whole, features of the face image are extracted through independent component analysis (ICA), and by applying the SVM, the reliability of face detection is improved to a small degree. However, this conventional technology generally uses nonlinear SVM in order to obtain a reliable face detection performance and this requires enormous amounts of computation such that the processing speed of the algorithm is slow.
Besides, the face detection apparatus applied to the conventional security monitoring systems can detect only a face rotated by a narrow angle roughly from −10 degrees to +10 degrees. However, in the image of a user approaching a security monitoring system such as a cash dispenser, face rotations of a large angle at at least one of the x, y and z axes are common and therefore, if the rotation angle is larger than a detectable rotation range, it is difficult for the system to detect a face. In addition, in the security monitoring system, face images of multiple channels by a plurality of cameras should be stored and a face detection speed of 15 frames or more per second is required. However, according to the conventional face detection technology to date, the maximum speed is only about 5 frames per second. When a face is detected by using skin color information, it is difficult to deal with an illumination change and when part of a face is covered by a hand, a mask or sunglasses, the face detection rate is greatly lowered.